CN113433825A - Self-adaptive fault-tolerant control method and system of single-link mechanical arm and storage medium - Google Patents

Self-adaptive fault-tolerant control method and system of single-link mechanical arm and storage medium Download PDF

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CN113433825A
CN113433825A CN202110689517.6A CN202110689517A CN113433825A CN 113433825 A CN113433825 A CN 113433825A CN 202110689517 A CN202110689517 A CN 202110689517A CN 113433825 A CN113433825 A CN 113433825A
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王建晖
黄堃锋
巩琪娟
马灿洪
严彦成
吴宇深
朱厚耀
张烨
洪嘉纯
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Guangzhou University
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Abstract

The invention discloses a self-adaptive fault-tolerant control method, a self-adaptive fault-tolerant control system and a storage medium for a single-link mechanical arm, wherein the control method comprises the following steps: establishing a failure nonlinear system model of the single-link mechanical arm; determining a virtual controller model and a self-adaptive law according to the failure nonlinear system model, the actual tracking error and the conversion function; the parameters of the transfer function include a preset settling time; determining a trigger controller model and a parameter updating law according to the failure nonlinear system model and the trigger event model; the parameters of the trigger event model include an update time; and acquiring a control input signal and preset stabilization time, and controlling the actuator of the single-link mechanical arm according to the virtual controller model, the self-adaptive law, the trigger controller model and the parameter updating law. The embodiment of the invention can be converged within the preset limited time, saves the network bandwidth, realizes global unified bounded convergence, and can be widely applied to the field of industrial automatic control.

Description

Self-adaptive fault-tolerant control method and system of single-link mechanical arm and storage medium
Technical Field
The invention relates to the field of industrial automatic control, in particular to a self-adaptive fault-tolerant control method and system for a single-link mechanical arm and a storage medium.
Background
With the development of internet technology and communication industry, Network Control Systems (NCSs) are widely applied to many practical systems, such as satellite communication, unmanned aerial vehicles, intelligent buildings, traffic networks, industrial control fields, and the like. Since the communication channel bandwidth is fixed, it cannot be increased in a short time and has limited computational power. In the conventional time-triggered control, the system state sampling and the controller updating are usually performed periodically, which results in waste of communication resources. In order to save network communication bandwidth and utilize bandwidth resources to the maximum extent, event trigger control has gained wide attention. However, the systems considered above are all assumed to be completely known; in the actual operation process of an industrial system, the actuator is difficult to avoid failure in different degrees. These actuator failures can cause system instability, limit tracking performance, and even cause catastrophic failures. As practical systems become more complex and more demanding, asymptotic convergence requiring infinite time has not been able to meet the needs of system operation, and various time-limited (FTC) control methods have emerged. However, in the existing finite time control method, specific stable time cannot be specified in advance, so that the research on the problem of constant time tracking control with an event trigger mechanism has important significance in theory and practice for controlling an uncertain nonlinear system with unknown control gain and an immeasurable state.
Interpretation of terms
Network Control Systems (NCSs): the control system is a control system in which the elements of the control loop exchange data over a communication network, and commands and feedback from the control system are transmitted in packets over the network.
The limited time control method comprises the following steps: the method means that the system state track reaches the equilibrium within the preset limit in the set time interval.
A reverse step design method: the feedback controller is obtained by recursively constructing the Lyapunov function of the closed-loop system, the control law is selected to enable the derivative of the Lyapunov function along the track of the closed-loop system to have certain performance, the boundedness and convergence of the track of the closed-loop system to a balance point are guaranteed, and the selected control law is a solution of the system stabilization problem, the tracking problem, the interference suppression problem or the combination of several problems.
Uncertain nonlinear systems: the system has the characteristics of both an uncertain system and a nonlinear system, namely the system with uncertain parameters, uncertain dynamics (system perturbation) and external interference, wherein the output of the system is not in direct proportion to the input of the system.
Time triggering: triggering is carried out at fixed intervals.
An event triggering mechanism: and determining whether to trigger according to the current state of the system, and performing various operations when the system state meets the trigger condition.
A neural network system: an arithmetic mathematical model simulating animal neural network behavior characteristics and performing distributed parallel information processing. The network achieves the aim of processing information by adjusting the mutual connection relationship among a large number of nodes in the network depending on the complexity of the system.
Partial Failure (PFs): the actuator only produces part of the effective output due to part damage or lack of power and the like.
Total Failures (TFs): complete failure of the actuator output due to complete damage or malfunction.
Self-adaptive compensation: the control method is a control method which designs a self-adaptive compensation control law according to the redundancy condition of a system actuator, achieves the control aim of tracking the motion of a reference model by utilizing an effective actuator and simultaneously keeps better dynamic and steady-state performances.
Sesamol behaviour (Zeno behavior): in event-triggered control, control is triggered an unlimited number of times within a limited time.
Disclosure of Invention
In view of the above, an object of the embodiments of the present invention is to provide a method, a system, and a storage medium for adaptive fault-tolerant control of a single link mechanical arm, which can converge within a preset limited time, save network bandwidth, and globally unify bounded convergence.
In a first aspect, an embodiment of the present invention provides a self-adaptive fault-tolerant control method for a single-link mechanical arm, including:
establishing a failure nonlinear system model of the single-link mechanical arm; the failure nonlinear system model comprises a known nonlinear system and an unknown nonlinear system;
determining a virtual controller model and a self-adaptive law according to the failure nonlinear system model, the actual tracking error and the conversion function; the parameters of the transfer function include a preset settling time;
determining a trigger controller model and a parameter updating law according to the failure nonlinear system model and the trigger event model; the parameters of the trigger event model include an update time;
and acquiring a control input signal and the preset stabilization time, and controlling the actuator of the single-link mechanical arm according to the virtual controller model, the self-adaptive law, the trigger controller model and the parameter updating law.
Optionally, the establishing a failure nonlinear system model of the single link mechanical arm includes:
establishing a known nonlinear system of the single-link mechanical arm about displacement and speed;
and establishing an unknown nonlinear system of complete failure or partial failure of the single-link mechanical arm actuator according to the neural network.
Optionally, the determining a virtual controller model and an adaptive law according to the failure nonlinear system model, the actual tracking error and the transfer function includes:
determining a virtual error model according to the failure nonlinear system model;
determining an error conversion model according to the actual tracking error and the conversion function;
and determining a virtual controller model and an adaptive law according to the error conversion model.
Optionally, the determining a virtual controller model and an adaptive law according to the error conversion model includes:
determining a first virtual controller model and a first self-adaptation law of the first virtual controller model according to the error conversion model;
and determining a second virtual controller model and a second adaptive law of the second virtual controller model according to the error conversion model.
Optionally, the determining a trigger controller model and a parameter updating law according to the failure nonlinear system model and the trigger event model includes:
determining a trigger event compensation model according to the failure nonlinear system model and the trigger event model;
and determining a trigger controller model and a parameter updating law according to the trigger event compensation model.
In a second aspect, an embodiment of the present invention provides an adaptive fault-tolerant control system for a single-link mechanical arm, including:
the system model establishing module is used for establishing a failure nonlinear system model of the single-link mechanical arm; the failure nonlinear system model comprises a known nonlinear system and an unknown nonlinear system;
the virtual controller model determining module is used for determining a virtual controller model and a self-adaptive law according to the failure nonlinear system model, the actual tracking error and the transfer function; the parameters of the transfer function include a preset settling time;
the trigger controller model determining module is used for determining a trigger controller model and a parameter updating law according to the failure nonlinear system model and the trigger event model; the parameters of the trigger event model include an update time;
and the comprehensive control module is used for acquiring a control input signal and the preset stabilization time, and controlling the actuator of the single-link mechanical arm according to the virtual controller model, the self-adaptive law, the trigger controller model and the parameter updating law.
In a third aspect, an embodiment of the present invention provides an adaptive fault-tolerant control system for a single-link mechanical arm, including:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is enabled to implement the adaptive fault-tolerant control method according to the embodiment of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a storage medium, in which a processor-executable program is stored, where the processor-executable program is used to execute the adaptive fault-tolerant control method described in the first aspect when executed by a processor.
In a fifth aspect, an embodiment of the present invention provides an adaptive fault-tolerant control system for a single link robot, including a controller, an actuator, and the single link robot, where the actuator is connected to the controller and the single link robot, and the controller includes:
the controller includes:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is enabled to implement the adaptive fault-tolerant control method according to the embodiment of the first aspect.
The implementation of the embodiment of the invention has the following beneficial effects: the embodiment of the invention carries out nonlinear transformation on the actual tracking error through the conversion function containing the preset stable time to determine the virtual controller model, thereby realizing convergence in the preset limited time; the trigger controller model is determined by the trigger event model including the update time, thereby achieving network bandwidth saving and globally uniform bounded convergence.
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Fig. 1 is a schematic flowchart illustrating steps of an adaptive fault-tolerant control method for a single-link robot according to an embodiment of the present invention;
FIG. 2 is a block diagram of an adaptive fault-tolerant control system for a single link robot according to an embodiment of the present invention;
FIG. 3 is a signal comparison graph of system output and reference output of a single link arm in accordance with an embodiment of the present invention during partial failure using an adaptive fault-tolerant control method in accordance with an embodiment of the present invention;
FIG. 4 is a signal comparison graph of normal control inputs and control inputs in the event of a partial failure for a single link robot arm according to an embodiment of the present invention;
FIG. 5 is a signal comparison graph of normal control input and control input during partial failure of a single link arm using the adaptive fault-tolerant control method of the embodiments of the present invention;
FIG. 6 shows a tracking error of a single link arm in a partial failure according to an adaptive fault-tolerant control method of an embodiment of the present invention;
FIG. 7 is a graph of event trigger and time interval for a single link arm in accordance with an embodiment of the present invention when the single link arm is partially failed using an adaptive fault-tolerant control method in accordance with an embodiment of the present invention;
FIG. 8 is a signal comparison graph of system output and reference output for a single link robotic arm in accordance with an embodiment of the present invention in the event of a complete failure using the adaptive fault-tolerant control method in accordance with an embodiment of the present invention;
FIG. 9 is a signal comparison of normal control inputs and full failure control inputs for a single link robot arm provided by embodiments of the present invention;
FIG. 10 is a signal comparison graph of normal control inputs and control inputs at full failure for a single link arm employing the adaptive fault-tolerant control method of embodiments of the present invention;
FIG. 11 illustrates a tracking error of a single link arm in the case of complete failure by using the adaptive fault-tolerant control method in the embodiment of the present invention;
FIG. 12 is a graph of event trigger and time interval for a single link arm in accordance with an embodiment of the present invention when failing completely using an adaptive fault-tolerant control method in accordance with an embodiment of the present invention;
FIG. 13 is a block diagram of an adaptive fault-tolerant control system for a single-link robotic arm according to an embodiment of the present invention;
fig. 14 is a block diagram of an adaptive fault-tolerant control system for a single link robot according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
As shown in fig. 1, an embodiment of the present invention provides an adaptive fault-tolerant control method for a single-link robot arm, which includes the following steps.
S100, establishing a failure nonlinear system model of the single-link mechanical arm; the failure nonlinear system model comprises a known nonlinear system and an unknown nonlinear system.
Specifically, the nonlinear system of the single-link mechanical arm is determined according to several types of uncertain nonlinear systems of the single-link mechanical arm, and the uncertain and invalid nonlinear systems are approximated through a neural network.
Optionally, the establishing a failure nonlinear system model of the single link mechanical arm includes:
establishing a known nonlinear system of the single-link mechanical arm about displacement and speed;
and establishing an unknown nonlinear system of complete failure or partial failure of the single-link mechanical arm actuator according to the neural network.
Specifically, for single link robotic arm systems, consider the following class of uncertain nonlinear systems:
Figure BDA0003125668090000051
wherein x isiE R, i is 1,2, …, n is the system state; u. ofj(t) is input to the system as R ( j 1,2, …, m); y e R is the output of the system,
Figure BDA0003125668090000052
is a known smooth non-linear function in the system; f. ofi(. epsilon. R, (i. 1, …, n) is an uncertain nonlinear function in the system;
Figure BDA0003125668090000053
bje.R is a parameter of known direction and unknown size.
Then, consider that the jth actuator may fail during operation, and therefore, at tiFThe instantaneous fault model of an instant can be expressed as:
Figure BDA0003125668090000054
wherein u isPFj(t) is an input to the system; rho is not less than 0j<1，tjFAnd uTFjAre all indeterminate constants.
In the present embodiment, in combination with a single link robotic arm, two degrees of actuator failure are considered: first, partial actuator failure (PFs), which means the actuator output uj(t) loses some performance in operation, so this has uj(t)＝ρjuPFj(t) and 0<ρj<1; second, complete actuator failure (TFs), which means the actuator output uj(t) is no longer subject to uPFj(t) influence of uTFjSo that this has uj(t)＝uTFjAnd ρj＝0。
In summary, the nonlinear system of the single link robot arm is designed as follows:
Figure BDA0003125668090000061
wherein x is1Denotes the displacement, x2Representing velocity, I is inertia, m is mass, G is gravitational acceleration, l is center of mass to joint length, B is viscous coefficient of friction, and d is set as barrierk＝sin(t)。
In particular, in the design of an adaptive control scheme, a neural network is used to approximate an unknown non-linear function. To clarify that
Figure BDA0003125668090000062
Is a known vector of basis functions, phi ═ phi1,Φ2]TIs a weight vector.
In order to facilitate the design of an adaptive controller and to obtain a suitable adaptive controller, in combination with the practical case of a single link robot system, the following assumptions are made:
assume that 1: at a time interval [ Tc,Tc+1) Where c is 0,1, …, and F, setting M represents total actuator failure, setting MPjRepresenting other actuators not all failing, M being clearPjUM ═ 1,2, …, m, and F is a natural number.
Assume 2: the maximum number of all failed actuators is m-1, but the maximum number of partial failed actuators can reach m, and the residual driving force can achieve the control purpose. Meanwhile, any actuator cannot generate state transition for many times.
Setting T0Assume that at time interval T, 0c,Tc+1) C is 0,1, …, F, and has mj(0≤mjM) actuator failures and no new actuator failures appear, obviously, at time interval T0,T1) All actuators can work normally, and new actuator failure can occur at T1After a moment. From hypothesis 2, TFIs finite, TF+1Is infinite.
Assume that 3: r is a bounded and known desired signal, and furthermore, r has a derivative of order n + 1.
S200, determining a virtual controller model and a self-adaptive law according to the failure nonlinear system model, the actual tracking error and the conversion function; the parameters of the transfer function include a preset settling time.
Specifically, to achieve the practically specified set-up time tracking performance, the following transfer function may be introduced:
Figure BDA0003125668090000071
wherein, 0<g < 1 and
Figure BDA0003125668090000072
is a positive design constant; 0<T<And ∞ represents the actually specified stabilization time.
It should be noted that, according to the definition of κ (t), the following several attributes related to the transformation are:
1. kappa (T) is the sum of
Figure BDA0003125668090000073
Is strictly increased, which means that
Figure BDA0003125668090000074
Is within t ∈ [0, ∞).
2. To ensure that a variable has the same initial value before and after conversion, it satisfies k (0) ═ 1.
3.κ (t) is bounded and differentiable, and
Figure BDA0003125668090000076
is bounded, which means that
Figure BDA0003125668090000077
Wherein, κmax>0 and kmaxIs a constant.
Optionally, the determining a virtual controller model and an adaptive law according to the failure nonlinear system model, the actual tracking error and the transfer function includes:
determining a virtual error model according to the failure nonlinear system model;
determining an error conversion model according to the actual tracking error and the conversion function;
and determining a virtual controller model and an adaptive law according to the error conversion model.
Optionally, the determining a virtual controller model and an adaptive law according to the error conversion model includes:
determining a first virtual controller model and a first self-adaptation law of the first virtual controller model according to the error conversion model;
and determining a second virtual controller model and a second adaptive law of the second virtual controller model according to the error conversion model.
In particular, to compensate for actuator failure in single link robotic arms, event-triggered adaptive compensation controllers based on a relative threshold strategy were designed. It is worth mentioning that the adaptive compensation control method adopts a back-stepping technique to track the unknown parameters in the measurement, wherein 2 recursion steps are designed, and the specific plan is as follows:
the following virtual error equation is introduced:
Figure BDA0003125668090000075
wherein alpha isi(i is 1,2) is a virtual control law, z isi(i is 1,2) is a variable error. The design procedure is explained in detail in the following steps.
In order to realize self-adaption and actual tracking within a specified stable time, a nonlinear conversion function kappa (t) is introduced to change an actual tracking error and a virtual error, and the converted actual tracking error e and the virtual error ziCan be defined as
Figure BDA0003125668090000081
First virtual controller alpha1Quilt coverThe method comprises the following steps:
Figure BDA0003125668090000082
wherein, c1Is a normal number which is a constant number,
Figure BDA0003125668090000083
is theta1Estimated value of theta1＝max{||Φ1||2,1}，
Figure BDA0003125668090000084
The first adaptation law is designed to:
Figure BDA0003125668090000085
Γ1is a positive definite matrix of r x r, gamma1Is a positive design parameter of the circuit board,
Figure BDA0003125668090000086
the following can thus be defined:
second virtual controller alpha2：
Figure BDA0003125668090000087
Second adaptation law:
Figure BDA0003125668090000088
wherein theta is2＝max{||Φ2||2,1}，
Figure BDA0003125668090000089
S300, determining a trigger controller model and a parameter updating law according to the failure nonlinear system model and the trigger event model; the parameters of the trigger event model include an update time.
In particular, due to the limited network resources of the communication network, in order to save network resources including communication channel bandwidth and computing power, the following event triggering scheme may be considered:
Figure BDA00031256680900000810
wherein, ω isj(t) is a control input, tk,k∈R+Representing an update time of the controller; e (t) ═ ωi(t)-uPFi(t) represents a measurement error; f (u)PFj(t)) is a function.
Optionally, the determining a trigger controller model and a parameter updating law according to the failure nonlinear system model and the trigger event model includes:
determining a trigger event compensation model according to the failure nonlinear system model and the trigger event model;
and determining a trigger controller model and a parameter updating law according to the trigger event compensation model.
In particular, in the event of a partial failure of the actuator in a single link robotic arm system, a longer update interval may be achieved by a larger threshold. When the system state tends to be stable, a shorter update interval can be obtained by a smaller threshold value, and then by a more accurate control signal uPFj(t) better system performance is obtained. Thus, a controller can be designed to compensate for measurement errors caused by the triggering mechanism, establishing an event-triggered strategy as shown below:
Figure BDA0003125668090000091
wherein,
Figure BDA0003125668090000092
indicating the measurement error, tk>0,k∈R+，ε∈R+,1>δ>0，
Figure BDA0003125668090000093
Are all positive parameters.
At time intervals tk,tk+1) Middle, omegaj(t)＝(1+λ1(t)δ)uPFj(t)+λ2(t)m1，λ1(t) and lambda2(t) are all time-varying constants satisfying lambda1(t) 1 or less and lambda2(t) is less than or equal to 1. By conversion, can be derived
Figure BDA0003125668090000094
In view of the parameter agnostics, the unknown parameter vector ψjIs estimated value of
Figure BDA0003125668090000095
For controlling law
Figure BDA0003125668090000096
So as to trigger the controller
Figure BDA0003125668090000097
The design is as follows:
Figure BDA0003125668090000098
wherein psij＝[ψj,1,ψj,21…,ψj,2m]T，
Figure BDA00031256680900000910
The parameter update law psi is set as:
Figure BDA0003125668090000099
wherein ξjIs a positive definite matrix.
And S400, acquiring a control input signal and the preset stabilization time, and controlling an actuator of the single-link mechanical arm according to the virtual controller model, the self-adaptive law, the trigger controller model and the parameter updating law.
As shown in fig. 2, the virtual controller model is applied to a virtual controller, and the virtual controller includes an adaptive law; the trigger controller model is applied to a trigger controller, and the trigger controller comprises a parameter updating law; the single-link mechanical arm in the embodiment of the invention comprises a first virtual controller and a first self-adaptive law corresponding to the first virtual controller, a second virtual controller and a second self-adaptive law corresponding to the second virtual controller, wherein the first virtual controller, the second virtual controller and a trigger controller jointly form a controller, and the controller controls an actuator.
It should be noted that the actuator may include several actuators.
To sum up: consider that all signals of the system are bounded for a period of time t*>0, thus performing interval t*≤{tk+1-tkAre all less than a bounded time t*，
Figure BDA0003125668090000101
Thus, Zeno behavior can be avoided. The following conclusions can be drawn by inference analysis: all signals in the system considered in a single link robot arm are bounded, effectively avoiding Zeno behavior. And the actual tracking error converges to the compact set xij＝{ej||ejIn | ≦ gA }, its attenuation rate
Figure BDA0003125668090000102
Indicating that the actual specified set-up time tracking performance has been achieved. Analysis of the specified settling time T can be done by adjusting T,
Figure BDA0003125668090000103
and g, affecting the tracking error and convergence speed of the system.
The implementation of the embodiment of the invention has the following beneficial effects: the embodiment of the invention carries out nonlinear transformation on the actual tracking error through the conversion function containing the preset stable time to determine the virtual controller model, thereby realizing convergence in the preset limited time; the trigger controller model is determined by the trigger event model including the update time, thereby achieving network bandwidth saving and globally uniform bounded convergence.
A specific simulation example is illustrated below.
Taking a single-link mechanical arm as an example, the model refers to the formula (3), and the system has two actuators in total, so that the failure conditions of different actuator mechanisms can be simulated.
The system parameters are as follows: the known and unknown functions are respectively
Figure BDA0003125668090000108
And
Figure BDA0003125668090000104
b j1, function of
Figure BDA0003125668090000105
The reference output signal is r ═ sin (t). To ensure that all signals within the system are bounded, the following parameters are designed: gamma-shaped1＝Γ2＝1，γ1＝γ2＝1，ξj＝1，a1＝a2＝1，c1＝34，c2＝15，H＝[0 0]T，σ＝40，δ＝0.2，
Figure BDA0003125668090000106
m1＝1.35，g＝0.3，T＝14，ε＝40，q＝1，m＝1，G＝9.8，l＝0.4，B＝1，
Figure BDA0003125668090000107
Assuming that the actuator 1 of the single-link mechanical arm has partial failure after 10 seconds, the actuator 2 operates normally, and the parameters are set as follows: ρ is 0.6.
In the case where the actuator 1 of the single link robot arm is partially failed after 10 seconds, the system output and reference output signals are as shown in fig. 3, the general control input is as shown in fig. 4, the control input with event triggering is as shown in fig. 5, the tracking error of the system is as shown in fig. 6, and the event triggering amounts and intervals thereof are as shown in fig. 7.
Simulation results show that: under the condition that the actuator 1 of the single-link mechanical arm has 40% of faults after 10 seconds, the input-output ratio is 60%, and the actuator 2 operates normally, as can be seen from fig. 3, the adaptive fault-tolerant control method in the embodiment of the invention realizes the tracking control of the single-link mechanical arm, and all signals of the system are bounded. As can be seen from fig. 4, the partially failed actuator system state is greatly affected. As can be seen from fig. 5, the control method proposed by the present invention has a smaller fluctuation amplitude of the partially failed actuator system than when the actuator is normally operated. The results in fig. 6 show that the tracking error fluctuates slightly after 10s, but the fluctuation range is within the expected error range, and the error of the system is stabilized within 5% after 0.2s, which proves that the system reaches the preset target state within the set time of 0.4 s.
In the case where the actuator 1 of the single link robot arm completely fails after 10 seconds, the system output and reference output signals are shown in fig. 8, the control input of the general control method is shown in fig. 9, the control input with event triggering is shown in fig. 10, the tracking error of the system is shown in fig. 11, and the event triggering amount and the interval thereof are shown in fig. 12.
Simulation results show that: in the case where the actuator 1 of the single link robot arm is completely disabled after 10 seconds and the actuator 2 is normally operated, as can be seen from fig. 8, the method realizes the tracking control of the single link robot arm, and all signals of the system are bounded. As can be seen from fig. 9, the fully disabled actuator system has entered a stuck state, and the output is no longer affected by the input. As can be seen from fig. 10, by the control method provided by the embodiment of the present invention, compared with the normal operation of the actuator, the actuator system which completely fails is affected to a certain extent. The results in fig. 11 show that the tracking error fluctuates greatly after 10s, but the fluctuation range is within the expected error range, and the error of the system is stabilized within 5% after 0.2s, which proves that the system reaches the preset target state within the set time of 0.4 s.
As can be seen from fig. 6 and 11, the tracking error is rapidly reduced to industrial 0.05, and then kept around 0, and the bandwidth can be saved from 0.02 to 0.19. Under the condition that one part of the actuator fails and the second actuator normally operates, the number of trigger events is changed along with the change of the control output in each 2-second time interval so as to reduce the consumption of bandwidth resources. In the conventional time trigger, the number of triggers per two seconds is 200, while in the event trigger related to the present invention, the average number of triggers per two seconds is 61, compared to a bandwidth resource saving rate of 69.5%. And under the condition that the first actuator completely fails and the second actuator normally operates, the number of trigger events is changed along with the change of the control output in each 2-second time interval so as to reduce the consumption of bandwidth resources. In the conventional time trigger, the number of triggers per two seconds is 200, while in the event trigger related to the present invention, the average number of triggers per two seconds is 59, compared to a bandwidth resource saving rate of 70.5%.
In summary, in a single link arm system with actuator failure, in combination with the proposed control method, a uniform presetting of the settling time can be achieved, so that the system has a limited convergence within a specified time. In addition, the system can realize self-adaptive compensation control, realize compensation rapidly, and under the mechanism of event trigger, can save the bandwidth resource effectively.
As shown in fig. 13, an embodiment of the present invention provides an adaptive fault-tolerant control system for a single-link robot arm, including:
the system model establishing module is used for establishing a failure nonlinear system model of the single-link mechanical arm; the failure nonlinear system model comprises a known nonlinear system and an unknown nonlinear system;
the virtual controller model determining module is used for determining a virtual controller model and a self-adaptive law according to the failure nonlinear system model, the actual tracking error and the transfer function; the parameters of the transfer function include a preset settling time;
the trigger controller model determining module is used for determining a trigger controller model and a parameter updating law according to the failure nonlinear system model and the trigger event model; the parameters of the trigger event model include an update time;
and the comprehensive control module is used for acquiring a control input signal and the preset stabilization time and controlling the actuator of the single-link mechanical arm according to the virtual controller model, the self-adaptive law, the trigger controller model and the parameter updating law.
It can be seen that the contents in the foregoing method embodiments are all applicable to this system embodiment, the functions specifically implemented by this system embodiment are the same as those in the foregoing method embodiment, and the advantageous effects achieved by this system embodiment are also the same as those achieved by the foregoing method embodiment.
As shown in fig. 14, an embodiment of the present invention provides an adaptive fault-tolerant control system for a single-link robot arm, including:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is caused to implement the adaptive fault-tolerant control method described above.
It can be seen that the contents in the foregoing method embodiments are all applicable to this system embodiment, the functions specifically implemented by this system embodiment are the same as those in the foregoing method embodiment, and the advantageous effects achieved by this system embodiment are also the same as those achieved by the foregoing method embodiment.
In addition, the embodiment of the application also discloses a computer program product or a computer program, and the computer program product or the computer program is stored in a computer readable storage medium. The computer program may be read by a processor of a computer device from a computer-readable storage medium, and the computer program is executed by the processor to cause the computer device to perform the above-described method. Likewise, the contents of the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the advantageous effects achieved by the present storage medium embodiment are also the same as those achieved by the above method embodiments.
As shown in fig. 2, an embodiment of the present invention provides an adaptive fault-tolerant control system for a single link robot, including a controller, an actuator and the single link robot, where the actuator is connected to the controller and the single link robot, and the controller includes: the controller includes:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is enabled to implement the adaptive fault-tolerant control method according to the embodiment of the first aspect.
It can be seen that the contents in the foregoing method embodiments are all applicable to this system embodiment, the functions specifically implemented by this system embodiment are the same as those in the foregoing method embodiment, and the advantageous effects achieved by this system embodiment are also the same as those achieved by the foregoing method embodiment.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (9)

1. A self-adaptive fault-tolerant control method of a single-link mechanical arm is characterized by comprising the following steps:
establishing a failure nonlinear system model of the single-link mechanical arm; the failure nonlinear system model comprises a known nonlinear system and an unknown nonlinear system;
determining a virtual controller model and a self-adaptive law according to the failure nonlinear system model, the actual tracking error and the conversion function; the parameters of the transfer function include a preset settling time;
determining a trigger controller model and a parameter updating law according to the failure nonlinear system model and the trigger event model; the parameters of the trigger event model include an update time;
and acquiring a control input signal and the preset stabilization time, and controlling the actuator of the single-link mechanical arm according to the virtual controller model, the self-adaptive law, the trigger controller model and the parameter updating law.
2. The adaptive fault-tolerant control method according to claim 1, wherein the establishing of the failure nonlinear system model of the single-link mechanical arm comprises the following steps:
establishing a known nonlinear system of the single-link mechanical arm about displacement and speed;
and establishing an unknown nonlinear system of complete failure or partial failure of the single-link mechanical arm actuator according to the neural network.
3. The adaptive fault-tolerant control method according to claim 1, wherein the determination of the virtual controller model and the adaptive law from the failed nonlinear system model, the actual tracking error and the transfer function comprises the steps of:
determining a virtual error model according to the failure nonlinear system model;
determining an error conversion model according to the actual tracking error and the conversion function;
and determining a virtual controller model and an adaptive law according to the error conversion model.
4. The adaptive fault-tolerant control method according to claim 3, wherein the determining of the virtual controller model and the adaptive law according to the error conversion model comprises the steps of:
determining a first virtual controller model and a first self-adaptation law of the first virtual controller model according to the error conversion model;
and determining a second virtual controller model and a second adaptive law of the second virtual controller model according to the error conversion model.
5. The adaptive fault-tolerant control method according to claim 1, wherein the determining of the trigger controller model and the parameter updating law according to the failure nonlinear system model and the trigger event model comprises the steps of:
determining a trigger event compensation model according to the failure nonlinear system model and the trigger event model;
and determining a trigger controller model and a parameter updating law according to the trigger event compensation model.
6. An adaptive fault-tolerant control system of a single-link mechanical arm, comprising:
the system model establishing module is used for establishing a failure nonlinear system model of the single-link mechanical arm; the failure nonlinear system model comprises a known nonlinear system and an unknown nonlinear system;
the virtual controller model determining module is used for determining a virtual controller model and a self-adaptive law according to the failure nonlinear system model, the actual tracking error and the transfer function; the parameters of the transfer function include a preset settling time;
the trigger controller model determining module is used for determining a trigger controller model and a parameter updating law according to the failure nonlinear system model and the trigger event model; the parameters of the trigger event model include an update time;
and the comprehensive control module is used for acquiring a control input signal and the preset stabilization time, and controlling the actuator of the single-link mechanical arm according to the virtual controller model, the self-adaptive law, the trigger controller model and the parameter updating law.
7. An adaptive fault-tolerant control system of a single-link mechanical arm, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the adaptive fault-tolerant control method of any of claims 1-5.
8. A storage medium having stored therein a processor-executable program, wherein the processor-executable program, when executed by a processor, is configured to perform the adaptive fault-tolerant control method of any one of claims 1 to 5.
9. The utility model provides a fault-tolerant control system of self-adaptation of single-link arm, its characterized in that includes controller, executor and single-link arm, the executor is connected the controller reaches single-link arm, the controller includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the adaptive fault-tolerant control method of any of claims 1-5.
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